Geography Reference
In-Depth Information
classes in the field and during revision exercises on the course expressed a feeling
of having ''done it all before''. We instead performed in situ training of the devices
in the ''real-world'' replacing the ''boredom'' of repeated information from pre-
paratory classes with greater enthusiasm and engagement. While the infrastructure
of the geocollaboratory requires significant initial preparation by staff it encour-
ages active learning through collaboration rather than passive learning through
observation.
The field course introduces a range of mobile data capture technologies to
satisfy the set of pedagogic objectives outlined earlier. Malta is chosen deliberately
to provide logistical challenges for the students as the familiar data acquisition
infrastructure of the UK is absent (together with fast internet access). The students
operate under the simple premise that they arrive on the island with no data and by
the end of the week will have acquired a range of datasets to support post-
fieldcourse analytical work.
A sequence of exercises begins with gaining familiarisation with consumer-
grade GPS devices for navigation and the capture of waypoints, tracklogs photos,
video and audio recordings for a virtual tourism experience. As the week pro-
gresses the use of mobile GIS solutions for land use surveying using hand-held
PDAs running Esri
ArcPad
integrated with ArcGIS
Server, differential GPS,
Real-time Kinematic GPS and other surveying techniques are introduced.
The dominant learning paradigm is data gathering with mobile devices in small
groups in Malta and then collaboration and data analysis at a later stage in a
desktop environment at the University. Historically, this data gathering process
has created significant difficulties in the field and led to problems in the analysis
stage if students have not collected data within a common ontology. The tools
demonstrated here are used heavily in the land use surveying exercises after being
introduced to students in the navigation exercise.
Fostering a collaborative data gathering approach is only partially served by
working in small groups (Drummond et al.
2006
) and prior experience has dem-
onstrated productivity and category agreement differences between different
groups of students. Student groups are spatially dispersed across a 2 9 2km
(1.6 9 1.6 mi) study area and historically without means of interaction, commu-
nication & collaboration. Subsequently they operate autonomously, gathering data
with different sampling strategies (e.g. classifying features using different object
types, capture resolutions, attribution and detail). These different collection and
classification strategies (their ontology of data collection) and/or a different
epistemologies about category types lead to inconsistencies when data are com-
bined as demonstrated by Gahegan and Brodaric (
2001
). Students' results are often
inaccurate
due
to
poor
quality
analysis
resulting
from
poor
data
capture
techniques.
Satisfying one of our objectives we sought to develop a mobile learning col-
laboratory where students could interact through their personal smartphones
(which were a collection of web-enabled devices from a range of manufacturers).
The use of the internet was critical to the development of this collaborative
learning environment and provided a suitable mechanism to visualize the progress
